{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMRQ53vLwCW1"
      },
      "source": [
        "# 安装，导入"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "_NjhcQwD7iuC"
      },
      "outputs": [],
      "source": [
        "from IPython.display import clear_output\n",
        "clear_output(wait=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "iuft1IhTwxI1"
      },
      "outputs": [],
      "source": [
        "import pennylane as qml\n",
        "from pennylane import numpy as np\n",
        "from pennylane.templates import RandomLayers\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ZXmiky3cxLr5"
      },
      "outputs": [],
      "source": [
        "train_data = np.array(pd.read_csv('train.csv'))\n",
        "test_data = np.array(pd.read_csv('test.csv'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9sG-3NjrxhY6",
        "outputId": "24064c47-f8bb-4afc-9452-86be53a880d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((250, 785), (65, 785))"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.shape, test_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "iJeRzYizBoH0"
      },
      "outputs": [],
      "source": [
        "train_len = len(train_data)\n",
        "test_len = len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKgyRKWXBvqE",
        "outputId": "435856ec-ff6e-492f-87c6-d32ad4326f23"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(250, 65)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_len, test_len"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "SO01Oum1x3Ek"
      },
      "outputs": [],
      "source": [
        "x_train = train_data[:, 0:784]\n",
        "y_train = train_data[:, 784]\n",
        "\n",
        "x_test = test_data[:, 0:784]\n",
        "y_test = test_data[:, 784]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K4VPdZ6bGWFX"
      },
      "source": [
        "### 这里的像素值在 0 到 1 范围内归一化。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "MvWfTKWIzzB4"
      },
      "outputs": [],
      "source": [
        "x_train = x_train / 255\n",
        "x_test = x_test / 255"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXuKzudK_l5z"
      },
      "source": [
        "### 最初每个图像数据都被展平为一个维度并上传到 csv。 所以在这里我们再次将数据重塑为 28x28 格式。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "oJqSOLPHz1IL"
      },
      "outputs": [],
      "source": [
        "x_train = x_train.reshape(250,28,28)\n",
        "x_test = x_test.reshape(65,28,28)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRCkaCj87Etd"
      },
      "source": [
        "# Quanvolution量子卷积"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUU7OFDf_9ic"
      },
      "source": [
        "### 这里为后面的层创建的量子卷积通道添加了一个额外的维度。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "o4hy9BJf4UBQ"
      },
      "outputs": [],
      "source": [
        "x_train = np.array(x_train[..., tf.newaxis], requires_grad=True)\n",
        "x_test = np.array(x_test[..., tf.newaxis], requires_grad=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCH9U_6k7X4R",
        "outputId": "f66c2ddb-ce27-4a7c-82db-c834e8d885dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((250, 28, 28, 1), (65, 28, 28, 1))"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.shape, x_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IiLOrWUlAL7Z"
      },
      "source": [
        "### 总共实施了四层量子卷积，如以下四个小节所示。\n",
        "\n",
        "### 在本讨论的后面部分，我们将忽略表示数据集中可用图像数量的第一个维度，并仅讨论图像的 (28x28x1) 格式。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jPNMFFFGC1BE"
      },
      "source": [
        "## Quanvolution Layer -1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yL8JGwcWBTYK"
      },
      "source": [
        "### 第一个 Quanvolutional 层在 (28x28x3) 图像数据上实现时，它会创建 (14x14x4) 维度的输出数据。\n",
        "\n",
        "### 量子电路应用于 2x2 图像块，每次返回 4 维输出。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "3m9pntK970Jr"
      },
      "outputs": [],
      "source": [
        "n_layers_quanv1 = 1\n",
        "\n",
        "dev_1 = qml.device(\"default.qubit\", wires=4)\n",
        "# Random circuit parameters\n",
        "quanv_1_params = np.random.uniform(high=2 * np.pi, size=(n_layers_quanv1, 4))\n",
        "\n",
        "@qml.qnode(dev_1)\n",
        "def circuit_1(phi):\n",
        "    # Encoding of 4 classical input values\n",
        "    for j in range(4):\n",
        "        qml.RY(np.pi * phi[j], wires=j)\n",
        "\n",
        "    # Random quantum circuit\n",
        "    RandomLayers(quanv_1_params, wires=list(range(4)))\n",
        "\n",
        "    # Measurement producing 4 classical output values\n",
        "    return [qml.expval(qml.PauliZ(j)) for j in range(4)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "aOGmfwfu9S6g"
      },
      "outputs": [],
      "source": [
        "def quanv_1(image):\n",
        "    \"\"\"Convolves the input image with many applications of the same quantum circuit.\"\"\"\n",
        "    out = np.zeros((14, 14, 4))\n",
        "\n",
        "    # Loop over the coordinates of the top-left pixel of 2X2 squares\n",
        "    for j in range(0, 28, 2):\n",
        "        for k in range(0, 28, 2):\n",
        "            # Process a squared 2x2 region of the image with a quantum circuit\n",
        "            q_results = circuit_1(\n",
        "                [\n",
        "                    image[j, k, 0],\n",
        "                    image[j, k + 1, 0],\n",
        "                    image[j + 1, k, 0],\n",
        "                    image[j + 1, k + 1, 0]\n",
        "                ]\n",
        "            )\n",
        "            # Assign expectation values to different channels of the output pixel (j/2, k/2)\n",
        "            for c in range(4):\n",
        "                out[j // 2, k // 2, c] = q_results[c]\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxstGJpy9gV5",
        "outputId": "2fededf7-92f6-4476-bc58-eee439b29718"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Quanvoltional(layer-1) preprocessing of train images:\n",
            "Train Image => 10/250        \n",
            "Train Image => 20/250        \n",
            "Train Image => 30/250        \n",
            "Train Image => 40/250        \n",
            "Train Image => 50/250        \n",
            "Train Image => 60/250        \n",
            "Train Image => 70/250        \n",
            "Train Image => 80/250        \n",
            "Train Image => 90/250        \n",
            "Train Image => 100/250        \n",
            "Train Image => 110/250        \n",
            "Train Image => 120/250        \n",
            "Train Image => 130/250        \n",
            "Train Image => 140/250        \n",
            "Train Image => 150/250        \n",
            "Train Image => 160/250        \n",
            "Train Image => 170/250        \n",
            "Train Image => 180/250        \n",
            "Train Image => 190/250        \n",
            "Train Image => 200/250        \n",
            "Train Image => 210/250        \n",
            "Train Image => 220/250        \n",
            "Train Image => 230/250        \n",
            "Train Image => 240/250        \n",
            "Train Image => 250/250        \n",
            "\n",
            "Quanvolutional(layer-1) preprocessing of test images:\n",
            "Test Image => 5/65        \n",
            "Test Image => 10/65        \n",
            "Test Image => 15/65        \n",
            "Test Image => 20/65        \n",
            "Test Image => 25/65        \n",
            "Test Image => 30/65        \n",
            "Test Image => 35/65        \n",
            "Test Image => 40/65        \n",
            "Test Image => 45/65        \n",
            "Test Image => 50/65        \n",
            "Test Image => 55/65        \n",
            "Test Image => 60/65        \n",
            "Test Image => 65/65        \n"
          ]
        }
      ],
      "source": [
        "x_train_quanv_1 = []\n",
        "x_test_quanv_1 = []\n",
        "\n",
        "\n",
        "print(\"Quanvoltional(layer-1) preprocessing of train images:\")\n",
        "for idx, img in enumerate(x_train):\n",
        "    if((idx+1)%10==0):\n",
        "        print(\"Train Image => {}/{}        \".format(idx + 1, train_len))\n",
        "    x_train_quanv_1.append(quanv_1(img))\n",
        "\n",
        "\n",
        "x_train_quanv_1 = np.array(x_train_quanv_1)\n",
        "\n",
        "\n",
        "\n",
        "print(\"\\nQuanvolutional(layer-1) preprocessing of test images:\")\n",
        "for idx, img in enumerate(x_test):\n",
        "    if((idx+1)%5==0):\n",
        "        print(\"Test Image => {}/{}        \".format(idx + 1, test_len))\n",
        "    x_test_quanv_1.append(quanv_1(img))\n",
        "\n",
        "\n",
        "x_test_quanv_1 = np.array(x_test_quanv_1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5TD-h84ERpt"
      },
      "source": [
        "### 在下面的单元格中，对应于每个主要 28x28 图像的所有 4 个通道已显示为四个图像。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "id": "k_QpLGlgB1UH",
        "outputId": "bde0027a-d2df-4334-942d-894313095507"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_samples = 4\n",
        "n_channels = 4\n",
        "fig, axes = plt.subplots(1 + n_channels, n_samples, figsize=(10, 10))\n",
        "for k in range(n_samples):\n",
        "    axes[0, 0].set_ylabel(\"Input\")\n",
        "    if k != 0:\n",
        "        axes[0, k].yaxis.set_visible(False)\n",
        "    axes[0, k].imshow(x_train[k, :, :, 0], cmap=\"gray\")\n",
        "\n",
        "    # Plot all output channels\n",
        "    for c in range(n_channels):\n",
        "        axes[c + 1, 0].set_ylabel(\"Output [ch. {}]\".format(c))\n",
        "        if k != 0:\n",
        "            axes[c, k].yaxis.set_visible(False)\n",
        "        axes[c + 1, k].imshow(x_train_quanv_1[k, :, :, c]*256, cmap=\"gray\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l4G0EwGvEAMO",
        "outputId": "8400b1f9-dfbb-4ef4-a887-5b41d832b238"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((250, 14, 14, 4), (65, 14, 14, 4))"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_quanv_1.shape, x_test_quanv_1.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GXYEdaLEAL-"
      },
      "source": [
        "## Quanvolution Layer -2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsRkAHaaB6_U"
      },
      "source": [
        "### 第二个量子卷积层在 (14x14x4) 图像数据上实现时，它会创建 (7x7x16) 维度的输出数据。\n",
        "\n",
        "### 量子电路应用于 2x2 图像块，每次返回与前一个相同的 4 维输出。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "g2SiZ0GEEAMQ"
      },
      "outputs": [],
      "source": [
        "n_layers_quanv2 = 1\n",
        "\n",
        "dev_2 = qml.device(\"default.qubit\", wires=4)\n",
        "# Random circuit parameters\n",
        "quanv_2_params = np.random.uniform(high=2 * np.pi, size=(n_layers_quanv2, 4))\n",
        "\n",
        "@qml.qnode(dev_2)\n",
        "def circuit_2(phi):\n",
        "    # Encoding of 4 classical input values\n",
        "    for j in range(4):\n",
        "        qml.RY(np.pi * phi[j], wires=j)\n",
        "\n",
        "    # Random quantum circuit\n",
        "    RandomLayers(quanv_2_params, wires=list(range(4)))\n",
        "\n",
        "    # Measurement producing 4 classical output values\n",
        "    return [qml.expval(qml.PauliZ(j)) for j in range(4)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "mfrzIQzhEAMR"
      },
      "outputs": [],
      "source": [
        "def quanv_2(image):\n",
        "    \"\"\"Convolves the input image with many applications of the same quantum circuit.\"\"\"\n",
        "    out = np.zeros((7, 7, 16))\n",
        "\n",
        "    # Loop over the coordinates of the top-left pixel of 2X2 squares\n",
        "    for j in range(0, 14, 2):\n",
        "        for k in range(0, 14, 2):\n",
        "            for l in range(4):\n",
        "                # Process a squared 2x2 region of the image with a quantum circuit\n",
        "                q_results = circuit_2(\n",
        "                    [\n",
        "                        image[j, k, l],\n",
        "                        image[j, k + 1, l],\n",
        "                        image[j + 1, k, l],\n",
        "                        image[j + 1, k + 1, l]\n",
        "                    ]\n",
        "                )\n",
        "                # Assign expectation values to different channels of the output pixel (j/2, k/2)\n",
        "                for c in range(4):\n",
        "                    out[j // 2, k // 2, (l*4)+c] = q_results[c]\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCLW-TnYEAMR",
        "outputId": "26a1a05b-dec7-4491-eeb5-250e953d551a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Quanvoltional(layer-2) preprocessing of train images:\n",
            "Train Image => 10/250        \n",
            "Train Image => 20/250        \n",
            "Train Image => 30/250        \n",
            "Train Image => 40/250        \n",
            "Train Image => 50/250        \n",
            "Train Image => 60/250        \n",
            "Train Image => 70/250        \n",
            "Train Image => 80/250        \n",
            "Train Image => 90/250        \n",
            "Train Image => 100/250        \n",
            "Train Image => 110/250        \n",
            "Train Image => 120/250        \n",
            "Train Image => 130/250        \n",
            "Train Image => 140/250        \n",
            "Train Image => 150/250        \n",
            "Train Image => 160/250        \n",
            "Train Image => 170/250        \n",
            "Train Image => 180/250        \n",
            "Train Image => 190/250        \n",
            "Train Image => 200/250        \n",
            "Train Image => 210/250        \n",
            "Train Image => 220/250        \n",
            "Train Image => 230/250        \n",
            "Train Image => 240/250        \n",
            "Train Image => 250/250        \n",
            "\n",
            "Quanvolutional(layer-2) preprocessing of test images:\n",
            "Test Image => 5/65        \n",
            "Test Image => 10/65        \n",
            "Test Image => 15/65        \n",
            "Test Image => 20/65        \n",
            "Test Image => 25/65        \n",
            "Test Image => 30/65        \n",
            "Test Image => 35/65        \n",
            "Test Image => 40/65        \n",
            "Test Image => 45/65        \n",
            "Test Image => 50/65        \n",
            "Test Image => 55/65        \n",
            "Test Image => 60/65        \n",
            "Test Image => 65/65        \n"
          ]
        }
      ],
      "source": [
        "x_train_quanv_2 = []\n",
        "x_test_quanv_2 = []\n",
        "\n",
        "\n",
        "print(\"Quanvoltional(layer-2) preprocessing of train images:\")\n",
        "for idx, img in enumerate(x_train_quanv_1):\n",
        "    if((idx+1)%10==0):\n",
        "        print(\"Train Image => {}/{}        \".format(idx + 1, train_len))\n",
        "    x_train_quanv_2.append(quanv_2(img))\n",
        "\n",
        "x_train_quanv_2 = np.array(x_train_quanv_2)\n",
        "\n",
        "\n",
        "\n",
        "print(\"\\nQuanvolutional(layer-2) preprocessing of test images:\")\n",
        "for idx, img in enumerate(x_test_quanv_1):\n",
        "    if((idx+1)%5==0):\n",
        "        print(\"Test Image => {}/{}        \".format(idx + 1, test_len))\n",
        "    x_test_quanv_2.append(quanv_2(img))\n",
        "\n",
        "x_test_quanv_2 = np.array(x_test_quanv_2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83v8Eh2WEtFn"
      },
      "source": [
        "### 在下面的单元格中，对应于每个主要 28x28 图像的所有 16 个通道已显示为四个图像。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rYPISrN3EAMS",
        "outputId": "e30f8c00-cd1c-4024-84ea-52c44714def4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x2880 with 68 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_samples = 4\n",
        "n_channels = 16\n",
        "fig, axes = plt.subplots(1 + n_channels, n_samples, figsize=(10, 40))\n",
        "for k in range(n_samples):\n",
        "    axes[0, 0].set_ylabel(\"Input\")\n",
        "    if k != 0:\n",
        "        axes[0, k].yaxis.set_visible(False)\n",
        "    axes[0, k].imshow(x_train[k, :, :, 0], cmap=\"gray\")\n",
        "\n",
        "    # Plot all output channels\n",
        "    for c in range(n_channels):\n",
        "        axes[c + 1, 0].set_ylabel(\"Output [ch. {}]\".format(c))\n",
        "        if k != 0:\n",
        "            axes[c, k].yaxis.set_visible(False)\n",
        "        axes[c + 1, k].imshow(x_train_quanv_2[k, :, :, c]*256, cmap=\"gray\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJu-PWkPK-I4",
        "outputId": "3aaf81c6-7dbd-426f-b919-9c845fa681ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((250, 7, 7, 16), (65, 7, 7, 16))"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_quanv_2.shape, x_test_quanv_2.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuV1jILPK-Il"
      },
      "source": [
        "## Quanvolution Layer -3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fip0ildOCLHB"
      },
      "source": [
        "### 第一个量子卷积层在 (7x7x16) 图像数据上实现时，它会创建 (3x3x64) 维度的输出数据。\n",
        "\n",
        "### 量子电路应用于图像的 3x3 块，每次返回 4 维输出，这里的块选择与第一层和第二层电路略有不同。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "2essPZiOK-I6"
      },
      "outputs": [],
      "source": [
        "n_layers_quanv3 = 1\n",
        "\n",
        "dev_3 = qml.device(\"default.qubit\", wires=4)\n",
        "# Random circuit parameters\n",
        "quanv_3_params = np.random.uniform(high=2 * np.pi, size=(n_layers_quanv3, 4))\n",
        "\n",
        "@qml.qnode(dev_3)\n",
        "def circuit_3(phi):\n",
        "    # Encoding of 4 classical input values\n",
        "    for j in range(4):\n",
        "        qml.RY(np.pi * phi[j], wires=j)\n",
        "\n",
        "    # Random quantum circuit\n",
        "    RandomLayers(quanv_3_params, wires=list(range(4)))\n",
        "\n",
        "    # Measurement producing 4 classical output values\n",
        "    return [qml.expval(qml.PauliZ(j)) for j in range(4)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "JDyVj8mKK-I6"
      },
      "outputs": [],
      "source": [
        "def quanv_3(image):\n",
        "    \"\"\"Convolves the input image with many applications of the same quantum circuit.\"\"\"\n",
        "    out = np.zeros((3, 3, 64))\n",
        "\n",
        "    # Loop over the coordinates of the top-left pixel of 2X2 squares\n",
        "    for j in range(0, 6, 2):\n",
        "        for k in range(0, 6, 2):\n",
        "            for l in range(16):\n",
        "                # Process a squared 2x2 region of the image with a quantum circuit\n",
        "                q_results = circuit_3(\n",
        "                    [\n",
        "                        image[j, k, l]*image[j+1, k+1, l],\n",
        "                        image[j, k + 2, l]*image[j+1, k+1, l],\n",
        "                        image[j + 2, k, l]*image[j+1, k+1, l],\n",
        "                        image[j + 2, k + 2, l]*image[j+1, k+1, l]\n",
        "                    ]\n",
        "                )\n",
        "                # Assign expectation values to different channels of the output pixel (j/2, k/2)\n",
        "                for c in range(4):\n",
        "                    out[(j+2) // 3, (k+2) // 3, (l*4)+c] = q_results[c]\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ojj6k-OVK-I7",
        "outputId": "674b12be-3f42-44a9-927c-01c5cd1cf022"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Quanvoltional(layer-3) preprocessing of train images:\n",
            "Train Image => 10/250        \n",
            "Train Image => 20/250        \n",
            "Train Image => 30/250        \n",
            "Train Image => 40/250        \n",
            "Train Image => 50/250        \n",
            "Train Image => 60/250        \n",
            "Train Image => 70/250        \n",
            "Train Image => 80/250        \n",
            "Train Image => 90/250        \n",
            "Train Image => 100/250        \n",
            "Train Image => 110/250        \n",
            "Train Image => 120/250        \n",
            "Train Image => 130/250        \n",
            "Train Image => 140/250        \n",
            "Train Image => 150/250        \n",
            "Train Image => 160/250        \n",
            "Train Image => 170/250        \n",
            "Train Image => 180/250        \n",
            "Train Image => 190/250        \n",
            "Train Image => 200/250        \n",
            "Train Image => 210/250        \n",
            "Train Image => 220/250        \n",
            "Train Image => 230/250        \n",
            "Train Image => 240/250        \n",
            "Train Image => 250/250        \n",
            "\n",
            "Quanvolutional(layer-3) preprocessing of test images:\n",
            "Test Image => 5/65        \n",
            "Test Image => 10/65        \n",
            "Test Image => 15/65        \n",
            "Test Image => 20/65        \n",
            "Test Image => 25/65        \n",
            "Test Image => 30/65        \n",
            "Test Image => 35/65        \n",
            "Test Image => 40/65        \n",
            "Test Image => 45/65        \n",
            "Test Image => 50/65        \n",
            "Test Image => 55/65        \n",
            "Test Image => 60/65        \n",
            "Test Image => 65/65        \n"
          ]
        }
      ],
      "source": [
        "x_train_quanv_3 = []\n",
        "x_test_quanv_3 = []\n",
        "\n",
        "\n",
        "print(\"Quanvoltional(layer-3) preprocessing of train images:\")\n",
        "for idx, img in enumerate(x_train_quanv_2):\n",
        "    if((idx+1)%10==0):\n",
        "        print(\"Train Image => {}/{}        \".format(idx + 1, train_len))\n",
        "    x_train_quanv_3.append(quanv_3(img))\n",
        "\n",
        "x_train_quanv_3 = np.array(x_train_quanv_3)\n",
        "\n",
        "\n",
        "\n",
        "print(\"\\nQuanvolutional(layer-3) preprocessing of test images:\")\n",
        "for idx, img in enumerate(x_test_quanv_2):\n",
        "    if((idx+1)%5==0):\n",
        "        print(\"Test Image => {}/{}        \".format(idx + 1, test_len))\n",
        "    x_test_quanv_3.append(quanv_3(img))\n",
        "\n",
        "x_test_quanv_3 = np.array(x_test_quanv_3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSYqOmEzExlc"
      },
      "source": [
        "### 在下面的单元格中，对应于每个主要 28x28 图像的前 20 个通道（在所有 64 个通道中）已显示为四个图像。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Of4_IfubK-I8",
        "outputId": "44c9db4b-9e5d-4f9e-8749-495527dd1c59"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x3600 with 84 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "n_samples = 4\n",
        "n_channels = 20\n",
        "fig, axes = plt.subplots(1 + n_channels, n_samples, figsize=(10, 50))\n",
        "for k in range(n_samples):\n",
        "    axes[0, 0].set_ylabel(\"Input\")\n",
        "    if k != 0:\n",
        "        axes[0, k].yaxis.set_visible(False)\n",
        "    axes[0, k].imshow(x_train[k, :, :, 0], cmap=\"gray\")\n",
        "\n",
        "    # Plot all output channels\n",
        "    for c in range(n_channels):\n",
        "        axes[c + 1, 0].set_ylabel(\"Output [ch. {}]\".format(c))\n",
        "        if k != 0:\n",
        "            axes[c, k].yaxis.set_visible(False)\n",
        "        axes[c + 1, k].imshow(x_train_quanv_3[k, :, :, c]*256, cmap=\"gray\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eAyonfQUvgU",
        "outputId": "17aa67ae-1b14-4541-e660-e227a946ecf4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((250, 3, 3, 64), (65, 3, 3, 64))"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_quanv_3.shape, x_test_quanv_3.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qv_sV7BoUvf-"
      },
      "source": [
        "## Quanvolution Layer -4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bg6LjQ4ECsdd"
      },
      "source": [
        "### 第一个量子卷积层在 (3x3x64) 图像数据上实现时，它会创建 (1x1x256) 维度的输出数据。\n",
        "\n",
        "### 量子电路应用于图像的 3x3 块，每次返回 4 维输出，这里的块选择与第一层或第二层电路几乎没有区别，与第三层电路类似。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "tvHPcBXtUvgW"
      },
      "outputs": [],
      "source": [
        "n_layers_quanv4 = 1\n",
        "\n",
        "dev_4 = qml.device(\"default.qubit\", wires=4)\n",
        "# Random circuit parameters\n",
        "quanv_4_params = np.random.uniform(high=2 * np.pi, size=(n_layers_quanv4, 4))\n",
        "\n",
        "@qml.qnode(dev_4)\n",
        "def circuit_4(phi):\n",
        "    # Encoding of 4 classical input values\n",
        "    for j in range(4):\n",
        "        qml.RY(np.pi * phi[j], wires=j)\n",
        "\n",
        "    # Random quantum circuit\n",
        "    RandomLayers(quanv_4_params, wires=list(range(4)))\n",
        "\n",
        "    # Measurement producing 4 classical output values\n",
        "    return [qml.expval(qml.PauliZ(j)) for j in range(4)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "0qxddIFcUvgX"
      },
      "outputs": [],
      "source": [
        "def quanv_4(image):\n",
        "    \"\"\"Convolves the input image with many applications of the same quantum circuit.\"\"\"\n",
        "    out = np.zeros((1, 1, 256))\n",
        "\n",
        "    # Loop over the coordinates of the top-left pixel of 2X2 squares\n",
        "    for j in range(0,2,2):\n",
        "        for k in range(0,2,2):\n",
        "            for l in range(64):\n",
        "                # Process a squared 2x2 region of the image with a quantum circuit\n",
        "                q_results = circuit_4(\n",
        "                    [\n",
        "                        image[j, k, l]*image[j+1, k+1, l],\n",
        "                        image[j, k + 2, l]*image[j+1, k+1, l],\n",
        "                        image[j + 2, k, l]*image[j+1, k+1, l],\n",
        "                        image[j + 2, k + 2, l]*image[j+1, k+1, l]\n",
        "                    ]\n",
        "                )\n",
        "                # Assign expectation values to different channels of the output pixel (j/2, k/2)\n",
        "                for c in range(4):\n",
        "                    out[(j+2) // 3, (k+2) // 3, (l*4)+c] = q_results[c]\n",
        "    return out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1IwcMKM5UvgZ",
        "outputId": "a23fb18a-3106-49a3-83d0-083f3a5eb47c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Quanvoltional(layer-4) preprocessing of train images:\n",
            "Train Image => 10/250        \n",
            "Train Image => 20/250        \n",
            "Train Image => 30/250        \n",
            "Train Image => 40/250        \n",
            "Train Image => 50/250        \n",
            "Train Image => 60/250        \n",
            "Train Image => 70/250        \n",
            "Train Image => 80/250        \n",
            "Train Image => 90/250        \n",
            "Train Image => 100/250        \n",
            "Train Image => 110/250        \n",
            "Train Image => 120/250        \n",
            "Train Image => 130/250        \n",
            "Train Image => 140/250        \n",
            "Train Image => 150/250        \n",
            "Train Image => 160/250        \n",
            "Train Image => 170/250        \n",
            "Train Image => 180/250        \n",
            "Train Image => 190/250        \n",
            "Train Image => 200/250        \n",
            "Train Image => 210/250        \n",
            "Train Image => 220/250        \n",
            "Train Image => 230/250        \n",
            "Train Image => 240/250        \n",
            "Train Image => 250/250        \n",
            "\n",
            "Quanvolutional(layer-4) preprocessing of test images:\n",
            "Test Image => 5/65        \n",
            "Test Image => 10/65        \n",
            "Test Image => 15/65        \n",
            "Test Image => 20/65        \n",
            "Test Image => 25/65        \n",
            "Test Image => 30/65        \n",
            "Test Image => 35/65        \n",
            "Test Image => 40/65        \n",
            "Test Image => 45/65        \n",
            "Test Image => 50/65        \n",
            "Test Image => 55/65        \n",
            "Test Image => 60/65        \n",
            "Test Image => 65/65        \n"
          ]
        }
      ],
      "source": [
        "x_train_quanv_4 = []\n",
        "x_test_quanv_4 = []\n",
        "\n",
        "\n",
        "print(\"Quanvoltional(layer-4) preprocessing of train images:\")\n",
        "for idx, img in enumerate(x_train_quanv_3):\n",
        "    if((idx+1)%10==0):\n",
        "        print(\"Train Image => {}/{}        \".format(idx + 1, train_len))\n",
        "    x_train_quanv_4.append(quanv_4(img))\n",
        "\n",
        "x_train_quanv_4 = np.array(x_train_quanv_4)\n",
        "\n",
        "\n",
        "\n",
        "print(\"\\nQuanvolutional(layer-4) preprocessing of test images:\")\n",
        "for idx, img in enumerate(x_test_quanv_3):\n",
        "    if((idx+1)%5==0):\n",
        "        print(\"Test Image => {}/{}        \".format(idx + 1, test_len))\n",
        "    x_test_quanv_4.append(quanv_4(img))\n",
        "\n",
        "x_test_quanv_4 = np.array(x_test_quanv_4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rk6BZigLFAIP"
      },
      "source": [
        "### 最后，在第 4 个量子卷积层结束时，从每个图像数据中创建了 256 个通道。 但是在这里显示所有 256 个频道是不可行的。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "FCcfHcPXUvga"
      },
      "outputs": [],
      "source": [
        "# n_samples = 4\n",
        "# n_channels = 20\n",
        "# fig, axes = plt.subplots(1 + n_channels, n_samples, figsize=(10, 50))\n",
        "# for k in range(n_samples):\n",
        "#     axes[0, 0].set_ylabel(\"Input\")\n",
        "#     if k != 0:\n",
        "#         axes[0, k].yaxis.set_visible(False)\n",
        "#     axes[0, k].imshow(x_train[k, :, :, 0], cmap=\"gray\")\n",
        "\n",
        "#     # Plot all output channels\n",
        "#     for c in range(n_channels):\n",
        "#         axes[c + 1, 0].set_ylabel(\"Output [ch. {}]\".format(c))\n",
        "#         if k != 0:\n",
        "#             axes[c, k].yaxis.set_visible(False)\n",
        "#         axes[c + 1, k].imshow(x_train_quanv_4[k, :, :, c]*256, cmap=\"gray\")\n",
        "\n",
        "# plt.tight_layout()\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHkeh8jpb31B",
        "outputId": "539b6383-9ad7-48bb-b358-6979d885b9c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((250, 1, 1, 256), (65, 1, 1, 256))"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_quanv_4.shape, x_test_quanv_4.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6n93G4ejbsei"
      },
      "source": [
        "# Flatten数据并将其保存为 npy 格式"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkcsDQkoFuqn"
      },
      "source": [
        "### 最后，代表每个图像的 (1x1x256) 维度数据已被展平为 256 的单一维度。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "vo9C9VkDb8CM"
      },
      "outputs": [],
      "source": [
        "x_train_final = x_train_quanv_4.reshape(250, 256)\n",
        "x_test_final = x_test_quanv_4.reshape(65, 256)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBamhJYfcM6H",
        "outputId": "99b357f6-23bb-4b22-e9c9-c971c0818432"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((250, 256), (65, 256))"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_final.shape, x_test_final.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Mx-CeqzAiJLx"
      },
      "outputs": [],
      "source": [
        "np.save(\"x_train_final.npy\", x_train_final)\n",
        "np.save(\"y_train.npy\", y_train)\n",
        "\n",
        "np.save(\"x_test_final.npy\", x_test_final)\n",
        "np.save(\"y_test.npy\", y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5KsBFz2F8BK"
      },
      "source": [
        "###  数据保存在四个.npy格式文件中，以便稍后访问分类器Model笔记本。 "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "MMRQ53vLwCW1"
      ],
      "name": "quanvolution_on_xray_image.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
